Database columns documentation

[maurerle]

One can use eralchemy2 to create mermaid plots like the one below:

classDiagram
class power_plant_meta{
   DOUBLE PRECISION efficiency
   DOUBLE PRECISION emission_factor
   TEXT index
   DOUBLE PRECISION max_power
   DOUBLE PRECISION min_power
   TEXT node
   TEXT simulation
   TEXT technology
   TEXT unit_operator
}
class demand_meta{
   TEXT index
   DOUBLE PRECISION max_power
   DOUBLE PRECISION min_power
   TEXT node
   TEXT simulation
   TEXT technology
   TEXT unit_operator
}
class market_meta{
   DOUBLE PRECISION demand_volume
   DOUBLE PRECISION demand_volume_energy
   BIGINT index
   TEXT market_id
   DOUBLE PRECISION max_price
   DOUBLE PRECISION min_price
   TEXT node
   TEXT only_hours
   DOUBLE PRECISION price
   TIMESTAMP product_end
   TIMESTAMP product_start
   TEXT simulation
   DOUBLE PRECISION supply_volume
   DOUBLE PRECISION supply_volume_energy
   TIMESTAMP time
}
class market_dispatch{
   TIMESTAMP datetime
   BIGINT index
   TEXT market_id
   DOUBLE PRECISION power
   TEXT simulation
   TEXT unit_id
}
class kpis{
   TEXT ident
   TEXT simulation
   DOUBLE PRECISION value
   TEXT variable
}
class market_orders{
   DOUBLE PRECISION accepted_price
   DOUBLE PRECISION accepted_volume
   TEXT bid_id
   TEXT bid_type
   TEXT block_id
   TIMESTAMP end_time
   TEXT exclusive_id
   TEXT link
   TEXT market_id
   DOUBLE PRECISION max_power
   DOUBLE PRECISION min_power
   TEXT node
   DOUBLE PRECISION price
   TEXT simulation
   TIMESTAMP start_time
   TEXT unit_id
   DOUBLE PRECISION volume
}
class storage_meta{
   DOUBLE PRECISION efficiency_charge
   DOUBLE PRECISION efficiency_discharge
   TEXT index
   DOUBLE PRECISION max_power_charge
   DOUBLE PRECISION max_power_discharge
   DOUBLE PRECISION min_power_charge
   DOUBLE PRECISION min_power_discharge
   TEXT node
   TEXT simulation
   TEXT technology
   TEXT unit_operator
}
class unit_dispatch{
   DOUBLE PRECISION demand_volume
   DOUBLE PRECISION demand_volume_energy
   DOUBLE PRECISION energy_cashflow
   DOUBLE PRECISION energy_marginal_costs
   TIMESTAMP index
   TEXT market_id
   DOUBLE PRECISION max_price
   DOUBLE PRECISION min_price
   TEXT node
   TEXT only_hours
   DOUBLE PRECISION power
   DOUBLE PRECISION price
   TEXT product_end
   TEXT product_start
   TEXT simulation
   TEXT snapshot
   DOUBLE PRECISION soc
   DOUBLE PRECISION supply_volume
   DOUBLE PRECISION supply_volume_energy
   TEXT time
   DOUBLE PRECISION total_costs
   TEXT unit
}
class rl_params{
   DOUBLE PRECISION actions_0
   DOUBLE PRECISION actions_1
   TIMESTAMP datetime
   BIGINT episode
   BOOLEAN evaluation_mode
   DOUBLE PRECISION exploration_noise_0
   DOUBLE PRECISION exploration_noise_1
   BOOLEAN learning_mode
   DOUBLE PRECISION profit
   DOUBLE PRECISION regret
   DOUBLE PRECISION reward
   TEXT simulation
   TEXT unit
}

Loading

For this one needs to run:

pip install eralchemy2
eralchemy2 -i "postgresql://assume:assume@localhost:5432/assume" -o "asssume_er.md"

(one needs to have graphviz installed or install eralchemy2 from conda though)

We could add something like this to our CI pipeline and update the ER diagram automatically.

@kim-mskw @nick-harder what do you think about that?